Metastasis is the cause of over 90% of cancer-related deaths. Cancer cells undergoing metastasis can switch dynamically between different phenotypes, enabling them to adapt to harsh challenges, such as overcoming anoikis and evading immune response. This ability, known as phenotypic plasticity, is crucial for the survival of cancer cells during metastasis, as well as acquiring therapy resistance. Various biochemical networks have been identified to contribute to phenotypic plasticity, but how plasticity emerges from the dynamics of these networks remains elusive. Here, we investigated the dynamics of various regulatory networks implicated in Epithelial-mesenchymal plasticity (EMP)-an important arm of phenotypic plasticity-through two different mathematical modelling frameworks: a discrete, parameter-independent framework (Boolean) and a continuous, parameter-agnostic modelling framework (RACIPE). Results from either framework in terms of phenotypic distributions obtained from a given EMP network are qualitatively similar and suggest that these networks are multi-stable and can give rise to phenotypic plasticity. Neither method requires specific kinetic parameters, thus our results emphasize that EMP can emerge through these networks over a wide range of parameter sets, elucidating the importance of network topology in enabling phenotypic plasticity. Furthermore, we show that the ability to exhibit phenotypic plasticity correlates positively with the number of positive feedback loops in a given network. These results pave a way toward an unorthodox network topology-based approach to identify crucial links in a given EMP network that can reduce phenotypic plasticity and possibly inhibit metastasis-by reducing the number of positive feedback loops.

中文翻译：

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使用基于网络拓扑的方法识别上皮间充质可塑性的抑制剂。

转移是超过 90% 的癌症相关死亡的原因。发生转移的癌细胞可以在不同的表型之间动态切换，使它们能够适应严峻的挑战，例如克服失巢凋亡和逃避免疫反应。这种被称为表型可塑性的能力对于癌细胞在转移过程中的存活以及获得治疗抗性至关重要。已确定各种生化网络有助于表型可塑性，但可塑性如何从这些网络的动态中出现仍然难以捉摸。在这里，我们通过两种不同的数学建模框架研究了与上皮间充质可塑性 (EMP) 相关的各种调节网络的动力学 - 一个重要的表型可塑性框架：离散的、与参数无关的框架 (布尔) 和连续的、参数不可知建模框架（RACIPE）。从给定 EMP 网络获得的表型分布方面，任一框架的结果在性质上相似，并表明这些网络是多稳定的，并且可以产生表型可塑性。这两种方法都不需要特定的动力学参数，因此我们的结果强调 EMP 可以通过这些网络在广泛的参数集上出现，阐明了网络拓扑在实现表型可塑性中的重要性。此外，我们表明表现出表型可塑性的能力与给定网络中正反馈回路的数量正相关。